Method and System for Low Latency Basket Calculation

ABSTRACT

A basket calculation engine is deployed to receive a stream of data and accelerate the computation of basket values based on that data. In a preferred embodiment, the basket calculation engine is used to process financial market data to compute the net asset values (NAVs) of financial instrument baskets. The basket calculation engine can be deployed on a coprocessor and can also be realized via a pipeline, the pipeline preferably comprising a basket association lookup module and a basket value updating module. The coprocessor is preferably a reconfigurable logic device such as a field programmable gate array (FPGA).

CROSS-REFERENCE AND PRIORITY CLAIM TO RELATED PATENT APPLICATION

The patent application is a continuation of U.S. patent application Ser.No. 12/013,302, filed Jan. 11, 2008, and entitled “Method and System forLow Latency Basket Calculation”, now U.S. Pat. No. ______, the entiredisclosure of which is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to the field of performing basketcalculation operations based on streaming data, particularly streamingfinancial market data.

Terminology

The following paragraphs provide several definitions for various termsused herein. These paragraphs also provide background informationrelating to these terms.

Financial Instrument: As used herein, a “financial instrument” refers toa contract representing an equity ownership, debt, or credit, typicallyin relation to a corporate or governmental entity, wherein the contractis saleable. Examples of financial instruments include stocks, bonds,commodities, currency traded on currency markets, etc. but would notinclude cash or checks in the sense of how those items are used outsidethe financial trading markets (i.e., the purchase of groceries at agrocery store using cash or check would not be covered by the term“financial instrument” as used herein; similarly, the withdrawal of $100in cash from an Automatic Teller Machine using a debit card would not becovered by the term “financial instrument” as used herein).Financial Market Data: As used herein, the term “financial market data”refers to data contained in or derived from a series of messages thatindividually represent a new offer to buy or sell a financialinstrument, an indication of a completed sale of a financial instrument,notifications of corrections to previously-reported sales of a financialinstrument, administrative messages related to such transactions, andthe like.Basket: As used herein, the term “basket” refers to a collectioncomprising a plurality of elements, each element having one or morevalues. The collection may be assigned one or more Net Values (NVs),wherein a NV is derived from the values of the plurality of elements inthe collection. For example, a basket may be a collection of data pointsfrom various scientific experiments. Each data point may have associatedvalues such as size, mass, etc. One may derive a size NV by computing aweighted sum of the sizes, a mass NV by computing a weighted sum of themasses, etc. Another example of a basket would be a collection offinancial instruments, as explained below.Financial Instrument Basket: As used herein, the term “financialinstrument basket” refers to a basket whose elements comprise financialinstruments. The financial instrument basket may be assigned one or moreNet Asset Values (NAVs), wherein a NAV is derived from the values of theelements in the basket. Examples of financial instruments that may beincluded in baskets are securities (stocks), bonds, options, mutualfunds, exchange-traded funds, etc. Financial instrument baskets mayrepresent standard indexes, exchange-traded funds (ETFs), mutual funds,personal portfolios, etc. One may derive a last sale NAV by computing aweighted sum of the last sale prices for each of the financialinstruments in the basket, a bid NAV by computing a weighted sum of thecurrent best bid prices for each of the financial instruments in thebasket, etc.GPP: As used herein, the term “general-purpose processor” (or GPP)refers to a hardware device having a fixed form and whose functionalityis variable, wherein this variable functionality is defined by fetchinginstructions and executing those instructions, of which a conventionalcentral processing unit (CPU) is a common example. Exemplary embodimentsof GPPs include an Intel Xeon processor and an AMD Opteron processor.Reconfigurable Logic: As used herein, the term “reconfigurable logic”refers to any logic technology whose form and function can besignificantly altered (i.e., reconfigured) in the fieldpost-manufacture. This is to be contrasted with a GPP, whose functioncan change post-manufacture, but whose form is fixed at manufacture.Software: As used herein, the term “software” refers to data processingfunctionality that is deployed on a GPP or other processing devices,wherein software cannot be used to change or define the form of thedevice on which it is loaded.Firmware: As used herein, the term “firmware” refers to data processingfunctionality that is deployed on reconfigurable logic or otherprocessing devices, wherein firmware may be used to change or define theform of the device on which it is loaded.Coprocessor: As used herein, the term “coprocessor” refers to acomputational engine designed to operate in conjunction with othercomponents in a computational system having a main processor (whereinthe main processor itself may comprise multiple processors such as in amulti-core processor architecture). Typically, a coprocessor isoptimized to perform a specific set of tasks and is used to offloadtasks from a main processor (which is typically a GPP) in order tooptimize system performance. The scope of tasks performed by acoprocessor may be fixed or variable, depending on the architecture ofthe coprocessor. Examples of fixed coprocessor architectures includeGraphics Processor Units which perform a broad spectrum of tasks andfloating point numeric coprocessors which perform a relatively narrowset of tasks. Examples of reconfigurable coprocessor architecturesinclude reconfigurable logic devices such as Field Programmable GateArrays (FPGAs) which may be reconfigured to implement a wide variety offixed or programmable computational engines. The functionality of acoprocessor may be defined via software and/or firmware.Hardware Acceleration: As used herein, the term “hardware acceleration”refers to the use of software and/or firmware implemented on acoprocessor for offloading one or more processing tasks from a mainprocessor to decrease processing latency for those tasks relative to themain processor.Bus: As used herein, the term “bus” refers to a logical bus whichencompasses any physical interconnect for which devices and locationsare accessed by an address. Examples of buses that could be used in thepractice of the present invention include, but are not limited to thePCI family of buses (e.g., PCI-X and PCI-Express) and HyperTransportbuses.Pipelining: As used herein, the terms “pipeline”, “pipelined sequence”,or “chain” refer to an arrangement of application modules wherein theoutput of one application module is connected to the input of the nextapplication module in the sequence. This pipelining arrangement allowseach application module to independently operate on any data it receivesduring a given clock cycle and then pass its output to the nextdownstream application module in the sequence during another clockcycle.

BACKGROUND AND SUMMARY OF THE INVENTION

The ability to closely track the value of financial instrument basketsthroughout a trading day as large volumes of events, such as trades andquotes, are constantly occurring is a daunting task. Every trade made ona financial instrument which is part of the portfolio of financialinstruments underlying a financial instrument basket will potentiallycause a change in that basket's value. The inventors herein believe thatconventional techniques used to compute basket values have been unableto keep up with the high volume of events affecting basket values,thereby leading to inaccuracy for financial instrument basket valuesrelative to the current state of the market because, with conventionaltechniques, the currently computed basket value will not be reflectiveof current market conditions, but rather market conditions as theyexisted anywhere from milliseconds to several seconds in the past.

As noted above, financial instrument baskets may represent standardindexes, ETFs, mutual funds, personal portfolios, etc.

An index represents the performance of a group of companies, wherein thegroup can be decided by various criteria such as market capitalization(e.g., the S&P 500), industry (e.g., the Dow Jones Industrial Average(DJIA)), etc.

A mutual fund is a professionally-managed form of collective investmentwherein the buyer pays the fund owner a certain amount of money and inexchange receives shares of the fund.

ETFs are very similar to mutual funds with an important difference beingthat ETFs can be traded continuously throughout the trading day on anexchange, just like stocks. Therefore, the prices of ETFs fluctuate notonly according to the changes in their underlying portfolios, but alsodue to changes in market supply and demand for the ETFs' sharesthemselves. Thus, ETFs provide the trading dynamics of stocks as well asthe diversity of mutual funds. Additionally, ETFs typically track awell-established market index, trying to replicate the returns of theindex. Personal portfolios are groupings of financial instruments thatare defined by an individual for his/her personal investment goals.

A financial instrument basket is defined by its set of underlyingfinancial instruments. The basket definition also specifies a weight foreach of the financial instruments within the basket. For example,consider a hypothetical basket denoted by B. Basket B contains Mfinancial instruments with symbols C_(i), wherein i=1, 2, . . . , MFurther, the weights of each financial instrument i in the basket isdenoted by w_(i), wherein i=1, 2, . . . , M. Associated with a basket isa measure of its monetary value known as its net asset value (NAV). Ifone assumes that the value at the current instant of each financialinstrument i within basket B is T_(i), wherein i=1, 2, . . . , M, thenthe NAV of basket B can be computed as:

$\begin{matrix}{{NAV} = {\frac{1}{d}{\sum\limits_{i = 1}^{M}{w_{i}T_{i}}}}} & (1)\end{matrix}$

wherein d is a divisor that is associated with the basket and discussedin greater detail hereinafter. Thus, equation (1) specifies that abasket's NAV is the weighted sum of the worth of the financialinstruments within the baskets, wherein the divisor acts as anormalizing factor.

The value of T represents a price for the financial instrument's shares.The price can be expressed as the last sale/trade price (last), the bestoffer to buy price (bid), the best offer to sell price (ask), orspecific offer prices to buy or sell (limit orders).

The value of w can be defined differently for each basket. For example,with capitalization-weighted indexes (such as the S&P 500), the weightof the financial instrument can be defined as the number of outstandingshares of the financial instrument multiplied by a “free-float” factor,wherein the free-float factor is a number between 0 and 1 that adjustsfor the fact that certain shares of the financial instrument are notpublicly available for trading. With an equal-weighted index (such asthe DJIA), the weight of a financial instrument is set to 1. With ETFsand personal portfolios, the weight of a financial instrument can be setequal to a specified number which reflects the fact that owning manyshares for each of the basket's financial instruments amounts to owningone share of that ETF or personal portfolio. Thus, in order to own oneshare of basket B, one would have to purchase w₁ shares of financialinstrument C₁, w₂ shares of financial instrument C₂, and so on up tow_(M) shares of financial instrument C_(M).

The value of d can also be defined differently for each basket. For anindex, the divisor can be set to the current index divisor, which can beeither the number of financial instruments in the index (e.g., 500 forthe S&P 500) or some other specified value. For ETFs, the divisor canchange during the trading day (as the divisor can also do with respectto indexes).

As indicated above, whenever there is a change in worth for one of abasket's underlying financial instruments, then the NAV for that basketshould be calculated in accordance with formula (1). However, financialmarket data messages from exchanges stream into a platform such as aticker plant at very high rates. For example, the options pricereporting authority (OPRA) expects that its message rate in January 2008will be approximately 800,000 messages/second. Each of these messagesmay potentially trigger multiple NAV recalculations. Moreover, becausethe same financial instrument may be a member of several baskets, theinventors herein estimate that, in order to track basket NAV values forall financial market data messages, the number of NAV calculations thatwill need to be performed every second will be at least on the order of10 million NAV calculations/second. The inventors herein believe thatconventional solutions are unable to keep up with such high update ratesdue to the serial nature of the conventional solutions, which constrainit to perform only one update at a time (including other tasks that asystem may need to perform unrelated to basket calculations).

This shortcoming is problematic because it is highly desirable forfinancial traders to be able to calculate the basket NAVs extremelyfast. An example will illustrate this desirability. As explained above,ETFs are baskets which can be traded like regular stocks on an exchange.The portfolio for an ETF is defined at the beginning of the trading dayand remains fixed throughout the trading day barring rare events. Sharesof ETFs can be created throughout the trading day by a creation processwhich works as follows. A buyer gives the owner of a fund the specifiedportfolio of financial instruments that make up the ETF and a cashpayment in an amount equal to the dividend payments on those financialinstruments (and possibly a transaction fee). The fund then issuesshares of the ETF to the buyer. Shares of ETFs can also be redeemed forthe underlying financial instruments in a similar manner: the investorgives the fund owner shares of the ETF (and possibly some cash as atransaction fee), and in return the investor receives the portfolio offinancial instruments that make up the ETF and cash equal to thedividend payments on those financial instruments.

During the trading day, the price of the shares of the ETF fluctuatesaccording to supply and demand, and the ETF's NAV changes according tothe supply and demand of the financial instruments in the ETF'sportfolio. It may well happen that, during the trading day, the tradingprice of the ETF deviates from its NAV. If the price of the ETF ishigher than the NAV, then the ETF is said to be trading “at a premium”.If the price of the ETF is lower than the NAV, then the ETF is said tobe trading “at a discount”. If a financial trader detects a discrepancybetween the ETF share price and the ETF NAV, then he/she can exploitthat discrepancy to make a profit by way of arbitrage.

In one scenario, assume that the ETF share price, denoted by T_(ETF), ishigher than the ETF's NAV. In this case, a trader can execute thefollowing steps:

-   -   Assemble a basket by purchasing the shares of the financial        instruments in the ETF's portfolio at a total cost approximately        equal to the NAV;    -   Create an ETF share by exchanging the assembled basket with the        fund owner; and    -   Sell the ETF on the exchange at a price approximately equal to        T_(ETF).        If the difference between T_(ETF) and the NAV is large enough to        offset the transaction costs and other cash payments, then the        trader can realize a profit by these actions. Moreover, this        profit is realized by riskless arbitrage (assuming these steps        could be performed instantaneously).

In another scenario, assume that the ETF share price, denoted byT_(ETF), is lower than the ETF's NAV. In this case, a trader canpurchase an ETF share, redeem it for the shares of the financialinstruments in the portfolio, and sell those financial instrument shareson the exchange. Once again, if the difference between T_(ETF) and theNAV is large enough to offset the transaction costs and other cashpayments, then the trader can realize a profit at virtually no risk.

The crucial element in profiting from such riskless arbitrages for ETFsis detecting the difference between the ETF's share price and the ETF'sNAV. The first trader to detect such discrepancies will stand to benefitthe most, and the latency in NAV computation becomes important—thecomputation of the NAV should be performed as quickly as possible tobeat competitors in the race toward detecting profit opportunities.

Independently of the ETF scenarios discussed above, another utility ofbasket NAV calculation is that the NAV provides a measure of how thebasket's portfolio is faring. A steadily increasing NAV indicates thatinvesting in more such baskets may be profitable. On the other hand, adecreasing NAV indicates that it may be desirable to sell the basketportfolio in order to avoid loss. This strategy is similar to ones thatwould be employed when trading in the shares of an individual financialinstrument with the basket providing a benefit by mitigating riskthrough diversification across a portfolio of financial instruments(e.g., if the basket as a whole has an increasing NAV, then investing inthat basket will be desirable independently of how any individualfinancial instrument in the basket is performing).

In an effort to satisfy a need in the art for high volume low latencybasket value computations, the inventors herein disclose a technique forcomputing basket values for a set of baskets based on the data within ahigh speed data stream as that data streams through a processor.

According to one aspect of a preferred embodiment of the presentinvention, the inventors disclose a technique for computing basketvalues wherein a coprocessor is used to perform the basket calculations.By offloading the computational burden of the basket calculations to acoprocessor, the main processor for a system can be freed to performdifferent tasks. The coprocessor preferably hardware accelerates thebasket calculations using reconfigurable logic, such as FieldProgrammable Gate Arrays (FPGAs). In doing so, a preferred embodimentpreferably harnesses the underlying hardware-accelerated technologydisclosed in the following patents and patent applications: U.S. Pat.No. 6,711,558 entitled “Associated Database Scanning and InformationRetrieval”, U.S. Pat. No. 7,139,743 entitled “Associative DatabaseScanning and Information Retrieval using FPGA Devices”, U.S. PatentApplication Publication 2006/0294059 entitled “Intelligent Data Storageand Processing Using FPGA Devices”, U.S. Patent Application Publication2007/0067108 entitled “Method and Apparatus for Performing BiosequenceSimilarity Searching”, U.S. Patent Application Publication 2008/0086274entitled “Method and Apparatus for Protein Sequence Alignment Using FPGADevices” (published from U.S. application Ser. No. 11/836,947, filedAug. 10, 2007), U.S. Patent Application Publication 2007/0130140entitled “Method and Device for High Performance Regular ExpressionPattern Matching”, U.S. Patent Application Publication 2007/0260602entitled “Method and Apparatus for Approximate Pattern Matching”, U.S.Patent Application Publication 2007/0174841 entitled “Firmware SocketModule for FPGA-Based Pipeline Processing”, U.S. Patent ApplicationPublication 2007/0237327 entitled “Method and System for High ThroughputBlockwise Independent Encryption/Decryption”, U.S. Patent ApplicationPublication 2007/0294157 entitled “Method and System for High SpeedOptions Pricing”, and U.S. Patent Application Publication 2008/0243675entitled “High Speed Processing of Financial Information Using FPGADevices”, the entire disclosures of each of which are incorporatedherein by reference.

According to another aspect of a preferred embodiment of the presentinvention, the inventors disclose that the different tasks involved insuch basket calculations can be relegated to different modules within apipeline. For example, one pipeline module can be configured todetermine which baskets are impacted by a current data event within thestream. Another pipeline module can be configured to receive informationabout the impacted baskets and compute data indicative of a new basketvalue for each of those impacted baskets. By pipelining these modulestogether, the two modules can work on different messages and/or basketsat the same time in parallel with each other.

In a preferred embodiment, this pipeline comprises a basket associationlookup module in communication with a basket value updating module. Thebasket association lookup module is configured to determine whichbaskets are impacted by each data message, while the basket valueupdating module is configured to compute at least one new value for eachimpacted basket based on the information within the data messages (forexample, in an embodiment operating on financial market data, thisinformation can be the financial instrument price information within thefinancial market data messages).

The basket value updating module preferably employs a delta calculationapproach to the computation of the updated basket values, as explainedin greater detail below. With such an approach, the number of arithmeticoperations needed to compute each basket value will remain the sameregardless of how many elements such as financial instruments aremembers of a given basket. Furthermore, the basket value updating modulemay optionally be configured to compute a plurality of different typesof basket values for the same basket simultaneously using parallelcomputation logic.

The basket association lookup module preferably accesses a basket settable which stores at least a portion of the data needed by the basketvalue updating module for the basket value calculations. In a preferredembodiment pertaining to financial information, this data can beindirectly indexed in the table by financial instrument such that, as anew message pertaining to a financial instrument is received, theappropriate data needed for the basket calculations can be retrieved. Toprovide the indirection, a second table is employed which indexespointers into the basket set table by financial instrument.

Furthermore, in an embodiment wherein the pipeline comprises a firmwarepipeline deployed on a coprocessor such as a reconfigurable logicdevice, the design of these modules can be tailored to the hardwarecapabilities of the coprocessor, thereby providing a level of synergythat streamlines the basket calculations so that the data streams can beprocessed at line speeds while a main processor is still free to performother tasks.

The inventors also note that the pipeline can employ a price eventtrigger module downstream from the basket value updating module. Theprice event trigger module can be configured to determine whether any ofthe updated basket values meet a client-specified trigger condition. Anyupdated basket values which meet the client-specified trigger conditioncan then be forwarded to the attention of the pertinent client (e.g., aparticular trader, a particular application, etc.). By doing so, clientscan take advantage of the low latency nature of the basket calculationpipeline to learn of trading opportunities which may be available.

Furthermore, the pipeline can employ an event generator moduledownstream from the price event trigger module, wherein the role of theevent generator module is to generate a message event for delivery tothe client in response to a trigger being set off by the price eventtrigger module. This message event preferably contains the updatedbasket value.

Further still, to limit the volume of financial market data messagesprocessed by the basket association lookup module (and its downstreammodules in the pipeline), the pipeline can employ a message qualifierfilter module upstream from the basket association lookup module.Preferably, the message qualifier filter module is configured to dropmessages from the financial market data message stream which are notpertinent to the basket calculations.

A pipeline such as the one described herein can be used in an embodimentof the invention to identify arbitrage conditions (such as the ETFarbitrage scenario discussed above) as they may arise in connection withfinancial instrument baskets and the current state of the markets. Bydetecting these arbitrage conditions quickly via the low latency basketcalculation pipeline, a trader is enabled to make trades on one or moreexchanges which take advantage of the detected arbitrage condition.

These and other features and advantages of the present invention will beapparent to those having ordinary skill in the art upon review of thefollowing description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1(a) and (b) depict exemplary embodiments for a system on whichhardware-accelerated basket calculations can be performed;

FIGS. 2(a) and (b) illustrate exemplary printed circuit boards for useas coprocessor 140;

FIG. 3 illustrates an example of how a firmware pipeline can be deployedacross multiple reconfigurable logic devices;

FIG. 4(a) is a high level block diagram view of how a coprocessor can beused to perform basket calculations;

FIG. 4(b) illustrates an exemplary process flow for computing an updatedbasket value using a delta calculation approach;

FIG. 5 depicts an exemplary basket calculation engine pipeline inaccordance with an embodiment of the invention;

FIG. 6 depicts an exemplary basket association lookup module;

FIG. 7 depicts an exemplary basket set pointer table;

FIG. 8 depicts an exemplary basket set table;

FIG. 9 depicts an exemplary basket value updating module;

FIGS. 10(a)-(c) depict exemplary embodiments for the NAV compute logicwithin the basket value updating module;

FIG. 11 depicts an exemplary embodiment of the basket value updatingmodule employing a plurality of NAV update engines;

FIG. 12 depicts an exemplary basket calculation engine pipeline inaccordance with another embodiment of the invention;

FIG. 13 depicts an exemplary price event trigger module;

FIG. 14 depicts an exemplary embodiment for the NAV-to-triggercomparison logic within the price event trigger module;

FIG. 15 depicts an exemplary embodiment for the price event triggermodule employing a plurality of client-specific price event triggermodules;

FIG. 16 depicts an exemplary basket calculation engine pipeline inaccordance with another embodiment of the invention;

FIG. 17 depicts an exemplary event generator module;

FIG. 18 depicts an exemplary basket calculation engine pipeline inaccordance with yet another embodiment of the invention;

FIG. 19 depicts an exemplary message qualifier filter module;

FIG. 20 depicts an exemplary basket calculation engine pipeline inaccordance with another embodiment of the invention; and

FIG. 21 depicts an exemplary ticker plant architecture in which a basketcalculation engine pipeline can be employed.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1(a) depicts an exemplary embodiment for system 100 configured toperform high speed basket calculations. Preferably, system 100 employs ahardware-accelerated data processing capability through coprocessor 140to perform basket calculations. Within system 100, a coprocessor 140 ispositioned to receive data that streams into the system 100 from anetwork 120 (via network interface 110). In a preferred embodiment,system 100 is employed to receive financial market data and performbasket calculations for financial instrument baskets. Network 120 thuspreferably comprises a network through which system 100 can access asource for financial data such as the exchanges themselves (e.g., NYSE,NASDAQ, etc.) or a third party provider (e.g., extranet providers suchas Savvis or BT Radians). Such incoming data preferably comprises aseries of financial market data messages, the messages representingevents such as trades and quotes relating to financial instruments.These messages can exist in any of a number of formats, as is known inthe art.

The computer system defined by processor 112 and RAM 108 can be anycommodity computer system as would be understood by those havingordinary skill in the art. For example, the computer system may be anIntel Xeon system or an AMD Opteron system. Thus, processor 112, whichserves as the central or main processor for system 100, preferablycomprises a GPP.

In a preferred embodiment, the coprocessor 140 comprises areconfigurable logic device 102. Preferably, data streams into thereconfigurable logic device 102 by way of system bus 106, although otherdesign architectures are possible (see FIG. 2(b)). Preferably, thereconfigurable logic device 12 is a field programmable gate array(FPGA), although this need not be the case. System bus 106 can alsointerconnect the reconfigurable logic device 102 with the processor 112as well as RAM 108. In a preferred embodiment, system bus 106 may be aPCI-X bus or a PCI-Express bus, although this need not be the case.

The reconfigurable logic device 102 has firmware modules deployedthereon that define its functionality. The firmware socket module 104handles the data movement requirements (both command data and targetdata) into and out of the reconfigurable logic device, thereby providinga consistent application interface to the firmware application module(FAM) chain 150 that is also deployed on the reconfigurable logicdevice. The FAMs 150 i of the FAM chain 150 are configured to performspecified data processing operations on any data that streams throughthe chain 150 from the firmware socket module 404. Preferred examples ofFAMs that can be deployed on reconfigurable logic in accordance with apreferred embodiment of the present invention are described below.

The specific data processing operation that is performed by a FAM iscontrolled/parameterized by the command data that FAM receives from thefirmware socket module 104. This command data can be FAM-specific, andupon receipt of the command, the FAM will arrange itself to carry outthe data processing operation controlled by the received command. Forexample, within a FAM that is configured to perform an exact matchoperation between data and a key, the FAM's exact match operation can beparameterized to define the key(s) that the exact match operation willbe run against. In this way, a FAM that is configured to perform anexact match operation can be readily re-arranged to perform a differentexact match operation by simply loading new parameters for one or moredifferent keys in that FAM. As another example pertaining to baskets, acommand can be issued to the one or more FAMs that make up a basketcalculation engine to add/delete one or more financial instrumentsto/from the basket.

Once a FAM has been arranged to perform the data processing operationspecified by a received command, that FAM is ready to carry out itsspecified data processing operation on the data stream that it receivesfrom the firmware socket module. Thus, a FAM can be arranged through anappropriate command to process a specified stream of data in a specifiedmanner. Once the FAM has completed its data processing operation,another command can be sent to that FAM that will cause the FAM tore-arrange itself to alter the nature of the data processing operationperformed thereby. Not only will the FAM operate at hardware speeds(thereby providing a high throughput of data through the FAM), but theFAMs can also be flexibly reprogrammed to change the parameters of theirdata processing operations.

The FAM chain 150 preferably comprises a plurality of firmwareapplication modules (FAMs) 150 a, 150 b, . . . that are arranged in apipelined sequence. However, it should be noted that within the firmwarepipeline, one or more parallel paths of FAMs 150 i can be employed. Forexample, the firmware chain may comprise three FAMs arranged in a firstpipelined path (e.g., FAMs 150 a, 150 b, 150 c) and four FAMs arrangedin a second pipelined path (e.g., FAMs 150 d, 150 e, 150 f, and 150 g),wherein the first and second pipelined paths are parallel with eachother. Furthermore, the firmware pipeline can have one or more pathsbranch off from an existing pipeline path. A practitioner of the presentinvention can design an appropriate arrangement of FAMs for FAM chain150 based on the processing needs of a given application.

A communication path 130 connects the firmware socket module 104 withthe input of the first one of the pipelined FAMs 150 a. The input of thefirst FAM 150 a serves as the entry point into the FAM chain 150. Acommunication path 132 connects the output of the final one of thepipelined FAMs 150 m with the firmware socket module 104. The output ofthe final FAM 150 m serves as the exit point from the FAM chain 150.Both communication path 130 and communication path 132 are preferablymulti-bit paths.

The nature of the software and hardware/software interfaces used bysystem 100, particularly in connection with data flow into and out ofthe firmware socket module are described in greater detail in theabove-referenced and incorporated U.S. Patent Application Publication2007/0174841.

FIG. 1(b) depicts another exemplary embodiment for system 100. In theexample of FIG. 1(b), system 100 includes a data store 142 that is incommunication with bus 106 via disk controller 114. Thus, the data thatis streamed through the coprocessor 140 may also emanate from data store142. Data store 142 can be any data storage device/system, but it ispreferably some form of mass storage medium. For example, data store 142can be a magnetic storage device such as an array of Seagate disks.

FIG. 2(a) depicts a printed circuit board or card 200 that can beconnected to the PCI-X or PCI-e bus 106 of a commodity computer systemfor use as a coprocessor 140 in system 100 for any of the embodiments ofFIGS. 1(a)-(b). In the example of FIG. 2(a), the printed circuit boardincludes an FPGA 102 (such as a Xilinx Virtex II FPGA) that is incommunication with a memory device 202 and a PCI-X bus connector 204. Apreferred memory device 202 comprises SRAM and DRAM memory. A preferredPCI-X or PCI-e bus connector 204 is a standard card edge connector.

FIG. 2(b) depicts an alternate configuration for a printed circuitboard/card 200. In the example of FIG. 2(b), a bus 206 (such as a PCI-Xor PCI-e bus), one or more disk controllers 208, and a disk connector210 are also installed on the printed circuit board 200. Any commoditydisk interface technology can be supported, as is understood in the art.In this configuration, the firmware socket 104 also serves as a PCI-X toPCI-X bridge to provide the processor 112 with normal access to anydisk(s) connected via the private PCI-X bus 206. It should be noted thata network interface can be used in addition to or in place of the diskcontroller and disk connector shown in FIG. 2(b).

It is worth noting that in either the configuration of FIG. 2(a) or2(b), the firmware socket 104 can make memory 202 accessible to the bus106, which thereby makes memory 202 available for use by an OS kernel asthe buffers for transfers to the FAMs from a data source with access tobus. It is also worth noting that while a single FPGA 102 is shown onthe printed circuit boards of FIGS. 2(a) and (b), it should beunderstood that multiple FPGAs can be supported by either including morethan one FPGA on the printed circuit board 200 or by installing morethan one printed circuit board 200 in the system 100. FIG. 3 depicts anexample where numerous FAMs in a single pipeline are deployed acrossmultiple FPGAs.

FIG. 4(a) depicts at a high level a coprocessor 140 that receives anincoming stream of new financial instrument prices, T_(j) ^(new), andcomputes new basket values, NAV^(new), in response to the receivedstream using a basket calculation engine 400. By offloading the basketcalculations to coprocessor 140, the system 100 can greatly decrease thelatency of calculating new basket values in response to new financialinstrument prices.

The basket calculation engine (BCE) 400 preferably derives itscomputation of NAV^(new) from formula (1). A direct implementation offormula (1) by BCE 400 would comprise M multiplications, M−1 additions,and 1 division. While a practitioner of the invention may choose toimplement such a direct use of formula (1) within BCE 400, a preferredembodiment for BCE 400 reduces the number of arithmetic operations thatneed to be performed to realize the function of formula (1). Given thatthe basis upon which the NAV for a basket is to be updated is a newprice for one of the basket's underlying financial instruments, itsuffices to calculate the contribution to the basket's NAV of thedifference between the new financial instrument price and the oldfinancial instrument price. This calculated contribution can then beadded to the old NAV value for the basket to find the updated NAV value.This process is referred to herein as a “delta calculation” for the NAV.This basis for this delta calculation approach is shown below, startingwith formula (1).

$\begin{matrix}{{{NAV}^{new} = {\frac{1}{d}{\sum\limits_{i = 1}^{M}{w_{i}T_{i}}}}}{{NAV}^{new} = {\left( {\frac{1}{d}{\sum\limits_{\underset{i \neq j}{i = 1}}^{M}{w_{i}T_{i}}}} \right) + \left( {\frac{1}{d}w_{j}T_{j}^{new}} \right)}}{{NAV}^{new} = {{\left( {\frac{1}{d}{\sum\limits_{\underset{i \neq j}{i = 1}}^{M}{w_{i}T_{i}}}} \right) + \left( {\frac{1}{d}w_{j}T_{j}^{new}} \right) + {\frac{1}{d}\left( {{w_{j}T_{j}^{old}} - {w_{j}T_{j}^{old}}} \right){NAV}^{new}}} = {{\frac{1}{d}\left( {\left( {\sum\limits_{\underset{i \neq j}{i = 1}}^{M}{w_{i}T_{i}}} \right) + {w_{j}T_{j}^{old}}} \right)} + {\frac{1}{d}\left( {{w_{j}T_{j}^{new}} - {w_{j}T_{j}^{old}}} \right)}}}}{{NAV}^{new} = {{\frac{1}{d}\left( {\left( {\sum\limits_{\underset{i \neq j}{i = 1}}^{M}{w_{i}T_{i}}} \right) + {w_{j}T_{j}^{old}}} \right)} + {\frac{w_{j}}{d}\left( {T_{j}^{new} - T_{j}^{old}} \right)}}}{{NAV}^{new} = {{NAV}^{old} + {\Delta \; j}}}} & (2)\end{matrix}$

Thus, it can be seen that NAV^(new) can be computed as the sum of theold NAV price, NAV^(old), and Δj, wherein Δj represents the deltacontribution to the NAV of the new financial instrument price, andwherein Δj is computed as:

$\begin{matrix}{{\Delta \; j} = {\frac{w_{j}}{d}\left( {T_{j}^{new} - T_{j}^{old}} \right)}} & (3)\end{matrix}$

Accordingly, it can be seen that the delta calculation approach canreduce the number of computations needed to calculate the new basketvalue from M multiplications, M−1 additions, and 1 division to only 1subtraction, 1 multiplication, 1 division, and 1 addition. Thisreduction can lead to powerful improvements in computational efficiencybecause a single basket may contain an arbitrary number of financialinstruments. It should be noted that, for some baskets, the value of Mmay be quite large. For example, a basket may be used to compute theWilshire 5000 stock index, which is an index that includes approximately6,300 securities. However, the presence of such a large number ofsecurities within the Wilshire 5000 stock index would not add to thecomputational latency of a BCE 400 which employs the delta calculationapproach because the NAV computation for such a basket will be based onformula (2) above.

FIG. 4(b) depicts a high level process flow for the computation of theupdated basket value by the BCE 400. At step 402, the BCE computes thedelta contribution Δj of the new financial instrument price T_(j) ^(new)to the basket value (see formula (3)). At step 404, the BCE computes theupdated basket value NAV^(new) from the computed delta contribution (seeformula (2)).

FIG. 5 depicts a preferred pipeline 500 for realizing the BCE 400.Preferably, pipeline 500 is deployed as a firmware pipeline 150 on areconfigurable logic device 102. The pipeline 500 preferably comprises abasket association lookup module 502 and a downstream basket valueupdating module 504. The basket association lookup module 502 receives astream of financial market data messages, wherein each messagerepresents a change in the bid, ask, and/or last price of a singlefinancial instrument. Each message preferably comprises at least asymbol identifier, a global exchange identifier, and one or more of alast price, a bid price, and an ask price for the financial instrumentcorresponding to the symbol identifier. The symbol identifier (or symbolID) preferably comprises a binary tag that uniquely identifies aparticular financial instrument. The global exchange identifier (GEID)preferably comprises a binary tag that uniquely identifies a particularexchange for which the message is relevant. Data tags within themessages preferably identify whether the price information within themessage pertains to a bid/ask/last price for the financial instrument.Also, the message fields are preferably normalized as between thedifferent message sources such prior to the time the messages reach thebasket association lookup module 502.

The basket association lookup (BAL) module 502 is configured todetermine, for each incoming message, a set of baskets which include thefinancial instrument that is the subject of that message. This basketset may comprise a plurality Q of baskets (although it should be notedthat only one basket may potentially be present within the basket set).Thus, the volume heavy nature of tracking basket values for eachfinancial market data message can be understood as each message causinga need for at least Q basket value calculations (a 1:Q expansion). In anexemplary embodiment, the maximum value for Q can be 1,024 baskets.However, other values could readily be used.

FIG. 6 depicts an exemplary BAL module 502. System 100 preferablymaintains a record for every known financial instrument, wherein eachrecord preferably contains the list of baskets which contain thatfinancial instrument, the relative weight of the financial instrumentwithin each basket on the list, and the most recent bid, ask, and lastsale price for the financial instrument. These records are preferablystored in a basket set table 608. System 100 also preferably maintains abasket set pointer table 604. Table 604 preferably comprises a set ofpointers and other information that point to appropriate records in thebasket set table 608. The use of the basket set pointer table 604 incombination with the basket set table 608 allows for more flexibility inmanaging the content of the basket set table 608. In an embodimentwherein the coprocessor employs a memory 202 such as that shown in FIGS.2(a) and (b), this memory 202 may comprise an SRAM memory device and anSDRAM memory device. Preferably, the basket set pointer table 604 can bestored in the SRAM memory device while the basket set table 608 can bestored in the SDRAM memory device. However, it should also be notedthat, for an embodiment of the coprocessor wherein one or more FPGAchips are used, either or both of tables 604 and 608 can be stored inavailable on-chip memory should sufficient memory be available.Furthermore, tables 604 and 608 could be stored in other memory deviceswithin or in communication with system 100.

In operation, the BAL module 502 performs a lookup in the basket setpointer table 604 using the symbol ID and GEID 600 for each message.Based on the symbol ID and GEID 600, a basket set pointer 606 isretrieved from table 604, and the BAL module 502 uses this basket setpointer 606 to perform a lookup in the basket set table 608. The lookupin the basket set table 608 preferably operates to identify the basketset 610 for the financial instrument identified by the symbol ID andidentify the stored last/bid/ask prices 612 for that financialinstrument. Each basket set comprises identifiers for one or morebaskets of which that financial instrument is a member.

A subtractor 616 preferably operates to subtract the retrievedlast/bid/ask prices 612 from the new last/bid/ask prices 602 containedin the current message to thereby compute the changes in thelast/bid/ask prices 614, as shown in FIG. 6. It should be noted thateach message may comprise one or more price fields for the subjectfinancial instrument. For any price fields not contained within themessage, the resultant price change is preferably treated as zero. Thus,if a message does not contain the last sale price for the financialinstrument, then the ΔLast price will be zero.

FIG. 7 depicts an exemplary embodiment for the basket set pointer table604 in greater detail. Table 604 comprises a plurality of records 700,wherein each record comprises a bit string corresponding to a set offlags 702, a bit string corresponding to a header block pointer 704, anda plurality of bit strings corresponding to different GEIDs 706. Eachrecord 700 is keyed by a symbol ID such that the BAL module 502 is ableto retrieve the record 700 from table 604 which corresponds to thesymbol ID of the current message. The flags 702 denote whether therecord 700 is valid (i.e., whether the subject financial instrument iscontained within at least one basket). The header block pointer 704serves as a pointer to a basket association record in table 608 for thefinancial instrument corresponding to the symbol ID.

Also, a given financial instrument may be fungible on multiple financialexchanges. The state of a financial instrument on a given exchange(e.g., the NYSE) is referred to as the regional view for that financialinstrument. The aggregate state of that financial instrument across allexchanges is referred to as the composite view for that financialinstrument. The composite value for the last price is preferably themost recent sales price for the financial instrument on any exchange.The composite value for the bid price is preferably the best of the bidprices for the financial instrument across all of exchanges on whichthat financial instrument is traded. The composite view for the askprice is preferably the best of the ask prices for the financialinstrument across all of exchanges on which that financial instrument istraded. Thus, the bid price and ask price in the composite view arecommonly referred to as the Best Bid and Offer (BBO) prices. Tables 604and 608 allow for baskets to be defined with regional and/or compositeviews of financial instruments. For example, basket A may contain thecomposite view of the 10 technology stocks with the largest marketcapitalization. Basket B may contain the NYSE regional view of the 10technology stocks with the largest market capitalization. Basket C maycontain the composite view for 10 technology stocks and the NYSEregional view for 10 industrial stocks.

To accommodate such flexible basket definitions, table 604 preferablyemploys a plurality of GEID fields 706 within each record 700. Each GEIDfield 706 serves as a further index into table 608 to retrieve theappropriate composite basket association record and/or regional basketassociation record for the pertinent financial instrument. Thus, BALmodule 502 uses the GEID of the current message to identify a matchingGEID field 706 in record 700. Field 706 ₀, which corresponds to GEID₀,is preferably used to identify any basket association records in table608 which correspond to the composite view for the financial instrument.The other fields 706 are used to identify any basket association recordsin table 608 which correspond to the region-specific view for thefinancial instrument. For example, GEID₁ may correspond to the NYSE viewfor the financial instrument while GEID_(m) may correspond to the NASDAQview for the financial instrument. The matching GEID field 706 is thenused as an additional index into table 608 to retrieve a set of basketassociation records for the financial instrument that are applicable toboth composite views and regional views of the financial instrument.

Preferably, BAL module 502 will always retrieve the composite GEID₀field applicable to a given financial instrument. Furthermore, for amessage with regional price information about a financial instrument,the BAL module 502 also operates to match the GEID field in the messagewith a GEID field 706 in record 700 for that financial instrument. Thus,the basket set pointer 606 used by the BAL module 502 preferablycomprises the header block pointer 704, the composite GEID index 706 ₀,and the regional GEID index 706 _(i) for the subject message.

FIG. 8 depicts an exemplary embodiment for the basket set table 608 ingreater detail. Table 608 comprises a plurality of basket associationrecords, wherein each record contains basket information for a differentfinancial instrument. Each record preferably comprises a plurality ofheader blocks 800 and possibly one or more extension blocks 822. Eachheader block 800, which preferably has a fixed size, corresponds to thesubject financial instrument and a particular GEID. Furthermore, eachheader block 800 preferably comprises a bit string corresponding to theGEID field 802 for that block, a bit string corresponding to anextension pointer 804, a bit string corresponding to a count field 806,a set of reserved bits 808, a bit string that corresponds to the mostrecently stored bid price 810 for the subject financial instrument, abit string that corresponds to the most recently stored ask price 812for the subject financial instrument, a bit string that corresponds tothe most recently stored last price for the subject financial instrument814, and a plurality of bit strings corresponding to a plurality of setsof basket information 816.

Preferably, each block 800 preferably comprises either composite basketassociation information or regional basket association information forthe subject financial instrument. Also, the blocks 800 are preferablylocated in memory such that the composite and regional blocks for agiven financial instrument are stored in contiguous memory addresses. Inthis way, the header block pointer 704 can be used to locate thefinancial instrument's composite block 800, while the GEID index 706 canbe used to locate the applicable regional block 800 for the financialinstrument by serving as an increment to the address found in the headerblock pointer 704.

Each basket information field 816 in table 608 preferably comprises abasket identifier and weight pair (Basket ID, Weight). The Basket IDserves to identify a particular basket while the weight serves toidentify the weight to be used when computing the delta contribution forsubject financial instrument to the identified basket.

Given that a single financial instrument may be present in a number ofdifferent baskets, it is possible that the number of (Basket ID, Weight)pairs needed for a given financial instrument will not fit within asingle block 800. To accommodate such overflow situations, table 608also preferably comprises a plurality of extension block records 822.Each extension block record 822 preferably has a fixed size andcomprises a bit string corresponding to an extension pointer 818, a bitstring corresponding to a count field 820, any one or more (Basket ID,Weight) pairs 816 as needed for the financial instrument. If the numberof (Basket ID, Weight) pairs 816 for the financial instrument are unableto fit within a single extension block 822, then the extension pointer818 will serve to identify the address of an additional extension block822 in which the additional (Basket ID, Weight) pairs 816 are found. Itshould thus be noted that a plurality of extension blocks 822 may beneeded to encompass all of a financial instrument's relevant (Basket ID,Weight) pairs 816. The count field 820 identifies how many (Basket ID,Weight) pairs 816 are present within the subject extension block 822.

The count field 806 within a block 800 serves to identify how many(Basket ID, Weight) pairs 816 are present within that block 800.

Fields 810, 812, and 814 contain the most recently stored bid/ask/lastprices for the subject financial instrument on the pertinent exchange.As shown by FIG. 6, this price information is used for computing theprice change values 614 (or delta prices) that are relevant to the deltacontribution calculation.

Thus, using the pointer 704 and GEID index reference 706 retrieved fromtable 604, the BAL module 502 is configured to access a block 800 intable 608 corresponding to the composite information relevant to thesubject message (e.g., the block 800 shown in FIG. 8 labeled with“GEID₀”) and a block 800 in table 608 corresponding to the regionalinformation relevant to the subject message (e.g., the record 800 shownin FIG. 8 labeled with “GEID₁”). To find the appropriate regional block,the BAL module uses the matching GEID field 706 to count down from thepertinent composite block to the appropriate regional block. Thus, ifthe pertinent regional block for a particular financial instrument isthe NYSE block, wherein the NYSE has a GEID value of that matches thesecond GEID field 706, GEID₂, in record 700, then the BAL module willfind the appropriate NYSE block 800 in table 608 two blocks down fromthe composite block.

In the example of FIG. 8, the regional information has an overflowcondition, and the extension pointer 804 points to an extension block822, as shown. Based on these lookups, the BAL module 502 retrieves thebid/ask/last prices and (Basket ID, Weight) pairs for the composite viewof the financial instrument as well as the bid/ask/last prices and(Basket ID, Weight) pairs for the appropriate regional view of thefinancial instrument. It should be noted that the retrieved bid/ask/lastprices 612 for the composite view will need to be associated with theretrieved list of (Basket ID, Weight) pairs 610 for the composite viewand the retrieved bid/ask/last prices 612 for the regional view willneed to be associated with the retrieved list of (Basket ID, Weight)pairs 610 for the regional view to ensure that downstream computationsare based on the appropriate values. To maintain this association, deltaevents as described below are preferably employed.

BAL module 502 is also preferably configured to update the records intable 608 with the new prices 602 in each message. This is shown byarrow 618 in FIG. 6, which represents a write operation into theappropriate fields of table 608. However, it should be noted that if themessage does not include a particular price field such as the last pricefield, then the BAL module 502 preferably maintains the last price field814 in the pertinent block 800 in its existing state.

It should be noted that the array of GEID fields 706 for each record 700in table 604 may be of fixed size for simplicity. Depending upon howmuch size a practitioner allocates to the GEID fields 706 in each record700, it may be the case that a financial instrument trades on asufficiently large number of exchanges that there are not a sufficientnumber of fields 706 for storing the financial instrument's relevantGEIDs. Thus, if this situation arises and the GEID 600 of the messagedoes not find a match to any GEID fields 706 in the pertinent record 700of table 604, then the BAL module 502, to retrieve an appropriateregional block from table 608, preferably indexes to the last headerblock 800 in the record for that financial instrument and conducts alinear search through the header blocks' GEID fields 802 to find a matchto the GEID 600 associated with the message.

It should also be noted that if the GEID 600 is not found in any of theGEID fields 802 for the financial instrument record in table 608, thismeans that the regional view of that financial instrument correspondingto GEID 600 is not included in any baskets.

It should also be noted that baskets may be dynamically changedthroughout the day by adding and/or removing (Basket ID, Weight) pairsfrom the basket association records in table 608 for financialinstruments.

To add a financial instrument to a basket, the system adds theappropriate (Basket ID, Weight) pair to that financial instrument'srecord in table 608. A control process, preferably performed in softwareexecuting on a GPP such as processor 112, preferably maintains a copy oftables 604 and 608 and computes where entries need to be added to thetable 608 (and possibly table 604 if the GEID for that financialinstrument is not present in the GEID fields 706 of record 700 for thatfinancial instrument) to reflect the addition of the financialinstrument to the basket. These determinations can be made using thesymbol ID and GEID for the financial instrument to be added. The controlprocess then preferably sends memory write commands to the pipeline forconsumption by the BAL module 502 that are effective to write updatetables 604 and 608 to reflect the addition of the subject financialinstrument to a basket. The control process may also be configured toimmediately send a synthetic event to the pipeline for consumption bythe BAL module 502 wherein this event contains the current prices forthe relevant financial instrument. This action will cause delta updatesto the NAV values for the subject basket wherein these delta updates arerelative to a zero (so the entirety of the price contribution isdetermined). However, it should also be noted that the control processmay be configured to simply wait for subsequent market events to arriveat the pipeline and allow the delta updates to be applied at that time.

Removing a financial instrument from a basket generally involvesremoving the (basket ID, weight) pair for the subject basket from therecord in table 608 for the subject financial instrument. Furthermore,to appropriately update the NAV values to reflect the deletion of thefinancial instrument from the basket, the control process preferablygenerates a synthetic event for delivery to the pipeline wherein theprice information for the subject financial instrument is zero. Thiscauses the delta price values 614 for the financial instrument to be thenegative value of the price information for that financial instrumentstored in table 608, thus removing the entirety of the pricecontribution for that financial instrument to the subject basket.

To add an entire basket to the system, it follows that the controlprocess can issue appropriate memory write commands to the pipeline thatadds (Basket ID, Weight) pairs to table 608 for all of the new basket'sconstituent financial instruments. The control process can also generatesynthetic events for delivery to the pipeline which reflects the currentprice information for these constituent financial instruments, asexplained above. Moreover, as can be understood in connection with FIGS.9 and 13 below, the control process can initiate the addition ofappropriate entries to the divisor table, NAV tables, client NAV tables,and client trigger threshold tables to reflect the addition of the newbasket to the system.

To delete an entire basket from the system, it also follows that thecontrol process can be configured to initiate a removal of the (BasketID, Weight) pairs from table 608 for all of the subject basket'sconstituent financial instruments. Similarly, the records in the tablesshown in FIGS. 9 and 13 can also be updated to remove those recordsassociated with the Basket ID of the removed basket.

The output from the BAL module 502 will be a stream of delta events.Each delta event preferably comprises a retrieved (Basket ID, Weight)pair 610 and at least one computed price change 614 for a financialinstrument corresponding to that retrieved (Basket ID, Weight) pair 610.In an embodiment of the invention, each delta event includes all of theprice deltas, even if one or more of the price deltas (e.g., the ΔBidprice) is zero. However, this need not be the case, as noted below.

It should also be understood that, for each message event that isreceived at the input to the BAL module 502, a plurality of delta eventsmay be produced at the output of the BAL module 502 due to the fact thatthe financial instrument which is the subject of the received messagemay be a member of a plurality of baskets. Partly because of thisexpansion, the low latency nature of pipeline 500 is particularlyadvantageous in allowing the task of updating basket values to keep upwith the inflow of events from the different exchanges.

Returning to FIG. 5, the basket value updating module 504 is configuredto compute the new basket value for each delta event produced by the BALmodule 502. FIG. 9 depicts an exemplary basket value updating (BVU)module 504.

A delta event buffer 900 buffers the delta events produced by the BALmodule 502. For each delta event read out of buffer 900, the BVU module504 performs a lookup in a divisor table 902 to determine theappropriate value for the divisor d from formula (3) for the basketcorresponding to the Basket ID within the current delta event. Thus, itcan be seen that table 902 preferably indexes each divisor value by theBasket ID for the divisor's corresponding basket. The delta event buffer900 and the divisor table 902 are preferably located within availableon-chip memory for the reconfigurable logic device 102. However, itshould be noted that buffer 900 and table 902 could alternatively bestored on any memory resource within or accessible to coprocessor 140.

A NAV update engine 950 then receives as an input the delta eventinformation (the (Basket ID, Weight) pair 610 and the delta bid/ask/lastprices 614) plus the divisor value d for that delta event to compute atleast one updated basket value NAV^(new). Preferably, the NAV updateengine 950 is configured to compute a plurality of different types ofbasket NAV's for each basket. For example, the NAV update engine 950 canbe configured to compute, in parallel, new NAVs based on bid prices, askprices, last prices, bid-tick prices, and ask+tick prices (BidNAV^(new), Ask NAV^(new), Last NAV^(new), Bid-Tick NAV^(new), andAsk+Tick NAV^(new) respectively), as shown in FIG. 9. In these examples,the tick value is defined as the minimum increment used by the pertinentexchange to measure a change in financial instrument price.

A demultipexer 904 operates to route portions of the delta eventinformation and the divisor value to the appropriate NAV compute logic916. The NAV update engine preferably employs a plurality of parallelcomputing paths to compute each type of basket NAV.

The computing path for computing Bid NAV^(new) preferably operates onthe Basket ID, Weight, Divisor, and ΔBid price. The BVU module 504performs a lookup in a Bid NAV table 906 based on the Basket ID toretrieve a stored Bid NAV value (Bid NAV^(old)) for the subject basket.This Bid NAV^(old) value serves as the NAV^(old) value in formula (2)during computation of Bid NAV^(new). NAV compute logic 916 then (i)computes the delta contribution Δj for the financial instrumentaccording to formula (3) using the divisor value as the d term informula (3), the Weight value as the w₁ term in formula (3), and theΔBid value as the (T_(j) ^(new)−−T_(j) ^(old)) term in formula (3), and(ii) thereafter computes Bid NAV^(new) according to formula (2) based onthe computed Δj value and the Bid NAV^(old) value.

The computing path for computing Ask NAV^(new) preferably operates inthe same manner as the computing path for Bid NAV^(new) albeitperforming a lookup in an Ask NAV table 908 based on the Basket ID andusing the ΔAsk price rather than the ΔBid price.

The computing path for computing Last NAV^(new) also preferably operatesin the same manner as the computing path for Bid NAV^(new) albeitperforming a lookup in an Last NAV table 910 based on the Basket ID andusing the ΔLast price rather than the ΔBid price.

Likewise, the computing paths for computing the Bid-Tick NAV^(new) andAsk+Tick NAV^(new) value preferably operate in the same manner as thecomputing path for Bid NAV^(new) albeit performing lookups in,respectively, a Bid-Tick NAV table 912 and an Ask+Tick NAV table 914based on the Basket ID and using, respectively, the ΔBid price and theΔAsk price.

Table 912 can store previously computed NAVs for each basket that arebased on a price for the financial instrument that is the previous bidprice for the financial instrument minus a tick value. Table 914 canstore previously computed NAVs for each basket that are based on a pricefor the financial instrument that is the previous ask price for thefinancial instrument plus a tick value.

Preferably, tables 906, 908, 910, 912, and 914 are stored in availableon-chip memory for the reconfigurable logic device 102. However, itshould be noted that these tables could alternatively be stored on anymemory resource within or accessible to coprocessor 140.

Thus, it can be seen that the NAV update engine 950 is preferablyconfigured to output, each clock cycle, an updated basket event, whereineach updated basket event comprises a plurality of different updatedNAVs for a particular basket together with a Basket ID for that basket.

The BVU module is also preferably configured to update tables 906, 908,910, 912, and 914 with the newly computed NAV values. Thus, the newlycomputed Bid NAV^(new) for a given Basket ID can be fed back into anentry for that Basket ID in table 906 so that the next delta eventpertaining to the same Basket ID will retrieve the most currentNAV^(old) value. The memory for tables 906, 908, 910, 912, and 914 ispreferably dual-ported, thus allowing for two read/write operations tooccur on each clock cycle. If updating logic for the BVU module ispipelined, it can be expected that contention for the same Basket IDentry in a given table during a read and write operation on the sameclock cycle will be rare and can be identified and avoided usingtechniques known in the art.

Also, a control process can issue a memory write command to the pipelinefor consumption by the BVU module to update an entry in the divisortable 902 in the event of a change in value for a basket's divisor. Sucha control process is preferably performed in software executing on a GPPsuch as processor 112.

FIG. 10(a) depicts an exemplary embodiment for the NAV compute logic916. A multiplier 1000 operates to compute the w_(j)(T_(j) ^(new)−T_(j)^(old)) portion of formula (3) using the Weight value and delta priceinformation from each delta event. A divider 1002 operates to divide thew_(j)(T_(j) ^(new)−T_(j) ^(old)) value by d to thereby compute Δjaccording to formula (3). Thereafter, an adder 1004 is used to add Δj tothe NAV^(old) value, thereby computing NAV^(new) according to formula(2).

FIG. 10(b) depicts another exemplary embodiment for the NAV computelogic 916. In the embodiment of FIG. 10(b), the division operation isperformed last, thereby improving the precision of the NAV computationrelative to the embodiment of FIG. 10(a). To re-order the arithmeticoperations such that the division operation inherent in formula (2) byway of formula (3) is performed last, the embodiment of FIG. 10(b)preferably adds a multiplication operation using multiplier 1006. Thismultiplication operation can be performed in parallel with themultiplication operation performed by multiplier 1000. Multiplier 1006operates to multiply the NAV^(old) value by d, thereby resulting in thevalue S^(old). In this embodiment, the delta contribution of thefinancial instrument can be expressed as w_(j)(T_(j) ^(new)−T_(j)^(old)) as shown at 1012 in FIG. 10(b). Adder 1004 then computes the sumof S^(old) and w_(j) (T_(j) ^(new)−T_(j) ^(old)) Given that S^(old)equals dNAV^(old) it can readily be seen that the act of dividingS^(old)+w_(j)(T_(j) ^(new)−T_(j) ^(old)) by d will result in NAV^(new)in accordance with formulas (2) and (3).

FIG. 10(c) depicts yet another exemplary embodiment for the NAV computelogic 916. In the embodiment of FIG. 10(c), the precision improvement ofthe FIG. 10(b) embodiment is preserved while also eliminating the needfor multiplier 1006. To do so, in a BVU module which employs the NAVcompute logic 916 of FIG. 10(c), the NAV tables 906, 908, 910, 912, and914 preferably store S^(old) values rather than NAV^(old) values,thereby eliminating the need for multiplier 1006. Furthermore, to updatetables 906, 908, 910, 912, and 914, the value of S^(new) (which is thesame as S^(old)+w_(j)(T_(j) ^(new)−T_(j) ^(old)) is fed back into therelevant tables via communication link 1010 such that S^(new) serves asS^(old) when the next delta event is processed.

It should be noted that the BVU module 504 can employ a plurality of NAVupdate engines 950 in parallel to thereby simultaneously compute aplurality of basket NAV types for a plurality of baskets. An example ofthis is shown in FIG. 11, wherein the BVU module 504 comprises twoparallel NAV update engines 950. In such an embodiment, the BVU module504 reads two delta events out of buffer 900 each clock cycle. Afterlookups are performed in the divisor table 902 to identify theappropriate divisor value for each delta event, routing logic 1100routes the delta event information and its associated divisor value toan appropriate NAV update engine 950. Such routing logic 1100 can beconfigured to assign different segments of the Basket ID range to eachset of parallel engines 950.

If desired, it should be noted that a single set of NAV tables 906, 908,910, 912, and 914 can be shared by a plurality of NAV update engines950. If the number of NAV update engines exceeds the number of availableports for reading from the NAV tables, then the BVU module 504 can beconfigured to interleave the accesses to these tables from the differentNAV update engines. These tables could also be replicated if desired toavoid such interleaving. The same can be said with respect to divisortable 902 (while FIG. 11 depicts an embodiment wherein two divisortables are utilized, it should be noted that a single divisor table 902may be shared by the different paths in FIG. 11).

FIG. 12 depicts an embodiment wherein pipeline 500 includes a priceevent trigger module 1200 in communication with the output from the BVUmodule 504. The price event trigger (PET) module 1200 is configured todetermine which updated basket values produced by the BVU module 504 areto be reported to an interested client. These determinations can be madeon the basis of any of a number of criteria. The client may be aparticular trader who has requested that he/she be notified when thevalue of a basket changes by a certain amount, or the client may beanother application within a trading system. The client may also beanother module deployed on the coprocessor 140 (such as another FAM 150if pipeline 500 is a FAM pipeline).

FIG. 13 depicts an exemplary embodiment for a PET module 1200.Preferably, PET module 1200 is configured to operate in parallel on eachbasket value type for a basket generated by the BVU module 504. Also, todetermine which of the updated basket values should be reported tointerested clients, the PET module 1200 preferably compares each updatedbasket value with a client-defined trigger threshold.

PET module 1200 preferably accesses a plurality of tables correspondingto different NAV types, wherein these tables store client referencevalues for each basket. These reference values are used by the PETmodule 1200 to judge whether new basket values should be reported tointerested clients. In an exemplary embodiment, the reference values areNAV values for the subject baskets which were previously reported to theclient. Thus, a client NAV table preferably exists for each basket valuetype generated by the BVU module 504. Thus, reference value table 1302stores a plurality of client Bid NAVs for the different baskets, whereineach client Bid NAV is indexed by a basket identifier. Reference valuetable 1304 stores a plurality of client Ask NAVs for the differentbaskets (each client Ask NAV being indexed by a basket identifier).Reference value table 1306 stores a plurality of client Last NAVs forthe different baskets (each client Last NAV being indexed by a basketidentifier). Reference value table 1308 stores a plurality of clientBid-Tick NAVs for the different baskets (each client Bid-Tick NAV beingindexed by a basket identifier). Reference value table 1310 stores aplurality of client Ask+Tick NAVs for the different baskets (each clientAsk+Tick NAV being indexed by a basket identifier). Each of these tablespreferably stores the most recent of their respective NAV types to bereported to the client. Thus, in the event of one of the triggers beingset off by a new basket value, the PET module is preferably configuredto update the NAV values in the client tables with the new basketvalues. Such updating logic can be configured to update the tables onlyfor those NAV values which set off the trigger or can be configured toupdate all of the tables when any NAV value sets off a trigger.

Also, a client NAV trigger threshold table preferably exists for eachbasket value type generated by the BVU module 504. Thus, table 1314stores a plurality of client Bid NAV trigger threshold values for thedifferent baskets, wherein each client Bid NAV trigger threshold valueis indexed by a basket identifier. Table 1316 stores a plurality ofclient Ask NAV trigger threshold values for the different baskets (eachclient Ask NAV trigger threshold value being indexed by a basketidentifier). Table 1318 stores a plurality of client Last NAV triggerthreshold values for the different baskets (each client Last NAV triggerthreshold value being indexed by a basket identifier). Table 1320 storesa plurality of client Bid-Tick NAV trigger threshold values for thedifferent baskets (each client Bid-Tick NAV trigger threshold valuebeing indexed by a basket identifier). Table 1322 stores a plurality ofclient Ask+Tick NAV trigger threshold values for the different baskets(each client Ask+Tick NAV trigger threshold value being indexed by abasket identifier).

Preferably, tables 1302, 1304, 1306, 1308, 1310, 1314, 1316, 1318, 1320,and 1322 are stored in available on-chip memory for the reconfigurablelogic device 102. However, it should be noted that these tables couldalternatively be stored on any memory resource within or accessible tocoprocessor 140.

Also, it should be noted that the client NAV tables and the client NAVtrigger threshold tables can be optionally consolidated if desired by apractitioner of this embodiment of the invention. In doing so, therecords in the client Bid NAV table 1302 would be merged with therecords in the client Bid NAV trigger threshold table 1314 such thateach record comprises the client Bid NAV and the client Bid NAV triggerthreshold value. Similarly, table 1304 could be merged with table 1316,table 1306 with table 1318, table 1308 with table 1320, and table 1310with table 1322.

PET module 1200 preferably employs a plurality of parallel computingpaths to determine whether any of the NAV types within each basket eventset off a client trigger.

A first computing path is configured to perform a lookup in the clientBid NAV table 1302 and a lookup in the client Bid NAV trigger thresholdtable 1314 based on the current updated basket event's Basket ID tothereby retrieve a client Bid NAV and a client Bid NAV trigger thresholdvalue. The client Bid NAV serves as a trigger against which the Bid NAVwithin the updated basket event is compared. The client Bid NAV triggerthreshold value is then used as the criteria to judge whether thetrigger has been set off. NAV-to-trigger comparison logic 1312 thenoperates perform this comparison and determine whether the trigger hasbeen set off, wherein a Bid NAV trigger flag indicates the result ofthis determination.

Additional computing paths perform these operations for the updatedbasket event's Ask NAV, Last NAV, Bid-Tick NAV, and Ask+Tick NAV toproduce Ask NAV, Last NAV, Bid-Tick NAV, and Ask+Tick NAV trigger flags,respectively, as shown in FIG. 13.

Trigger detection logic 1324 receives the different NAV trigger flagsproduced by the computing paths. This logic 1324 is configured todetermine whether any of the trigger flags for a given updated basketevent are high. Preferably, the trigger detection logic 1324 isconfigured pass an updated basket event as an output if any of the flagsfor that updated basket event are high. As such, it can be seen that thePET module 1200 serves as a filter which passes updated basket eventsthat are deemed noteworthy according to client-defined criteria.

FIG. 14 illustrates an exemplary embodiment for the comparison logic1312. A subtractor 1400 operates to subtract the retrieved client NAVfrom the updated basket event's NAV (NAV^(new)). A comparator 1404 thencompares the difference between NAV^(new) and the client NAV with theretrieved client trigger threshold to determine whether the NAV triggerflag should be set. If the difference exceeds the threshold value, thencomparator preferably sets the NAV trigger flag to high.

Thus, a client may want to be notified whenever the Bid NAV of Basket145 changes by $0.05, whenever the Ask NAV for Basket 145 changes by$0.03, or whenever the Last NAV for Basket 145 changes by $0.02. In suchan instance, the client Bid NAV trigger threshold value in table 1314for Basket 145 can be set equal to $0.05, the client Ask NAV triggerthreshold value in table 1316 for Basket 145 can be set equal to $0.03,and the client Last NAV trigger threshold value in table 1318 for Basket145 can be set equal to $0.02. If the new bid NAV, Ask NAV, and Last NAVvalues within the updated basket event for Basket 145 are $20.50,$20.40, and $20.20 respectively, while the stored values for the clientNAVs of Basket 145 are $20, $20.42, and $20.19 respectively, then thecomparison logic will operate to set the Bid NAV trigger flag high, theAsk NAV trigger flag low, and the Last NAV trigger flag high.

However, it should be noted that the comparison logic 1312 could beconfigured to determine whether a client trigger has been set off in anyof a number of ways. For example, the client threshold values can beexpressed in terms of percentages if desired (with correspondingadjustments in the comparison logic to compute the percentage differencebetween the new NAV and the client NAV). Also, the trigger conditioncould be based on a tracking instrument that is different than thesubject NAV. For example, the trigger condition for a given basket canbe defined in relation to a stored price for the DJIA. The client NAVtables would then store a DJIA price for that basket, and that basket'sNAV prices would be compared against the retrieved DJIA price todetermine whether the trigger threshold has been reached.

It should be noted that the PET module 1200 can be configured to detectarbitrage conditions by appropriately populating the client NAV tablesand the client trigger threshold tables. For example, with ETFs, aclient NAV table can be populated with the current share price of theETF itself (T_(ETF)). Thus, the client bid NAV table could be populatedwith the current bid price for a share of the ETF, the client ask NAVtable could be populated with the current ask price for a share of theETF, the client last NAV table could be populated with the current lastprice for a share of the ETF, etc. By populating the client NAV tablesin this manner, the PET module can detect when there is a discrepancybetween the share price of the ETF and the net asset value of the ETF'sconstituent financial instruments. Such a discrepancy can be takenadvantage of by a trader, as explained above.

Thus, a client application that is informed by the pipeline of adiscrepancy between T_(ETF) and the ETF's NAV can place one or moretrades on one or more financial markets to realize a profit from thedetected arbitrage condition. For example, if the ETF's NAV is less thanthe ETF's share price, a client application can execute the followingsteps:

-   -   Assemble a basket by purchasing the shares of the financial        instruments in the ETF's portfolio at a total cost approximately        equal to the NAV;    -   Create an ETF share by exchanging the assembled basket with the        fund owner; and    -   Sell the ETF on the exchange at a price approximately equal to        T_(ETF).        Furthermore, if the ETF's NAV is greater than the ETF's share        price, a client application can execute the above steps in        reverse. That is, a trader can buy one or more shares of the        subject ETF from the market, exchange the ETF for shares of its        constituent financial instruments, and then sell those shares of        the constituent financial instruments on the market.

It should also be noted that the trigger thresholds in the clienttrigger threshold tables can be populated with values that take intoaccount any expected transactional costs involved in the process ofmaking such trades to take advantage of the price discrepancy, asexplained below. Thus, if the sales price for the assembled ETF exceedsthe aggregate prices of the financial instrument shares purchased toassemble the basket and the related transactional costs in doing so (orthe sales prices for the constituent financial instruments exceeds thepurchase price of the ETF and the related transactional costs), a traderwho uses the client application can yield a profit by way of arbitrage.For such actions to work, the inventors note that time is of theessence, and it is believed that the low latency nature of the disclosedpipeline greatly increases a trader's ability to recognize and profitfrom such arbitrage conditions as they arise in a constantly fluctuatingmarket.

Moreover, it should be noted that the PET module 1200 can filter updatedbasket events for a plurality of different clients. In this manner, eachsubscribing client can define its own set of reference values andtrigger thresholds. To accomplish such a feature, each updated basketevent can be compared in parallel with a plurality of criteria fordifferent clients via a plurality of client-specific PET modules 1500,as shown in FIG. 15. It should also be noted that the client-specificPET modules 1500 (each of which would be a client-specific replicant ofPET module 1200 of FIG. 13) can be arranged in series (wherein eachclient-specific PET module would optionally add a client-specific flagto each updated basket event to thereby indicate whether that client'striggering criteria were met). Furthermore, the client-specific PETmodules 1500 could also be arranged in a hybrid parallel/seriesarrangement if desired.

FIG. 16 depicts an embodiment wherein pipeline 500 includes an eventgenerator module 1600 in communication with the output from the PETmodule 1200. The event generator module 1600 is configured to processeach incoming triggered basket event to construct a normalized outputmessage event which contains the computed new NAVs within the triggeredbasket event.

FIG. 17 depicts an exemplary embodiment for the event generator module1600. Preferably, the event generator module 1600 is configured toinsert a bit string into each output event 1704 which defines aninterest list for that event, wherein the interest list identifies eachclient that is to be notified of the event. Preferably, this interestlist takes the form of a bit vector, wherein each bit positioncorresponds to a different client. For any bit positions that are high,this means that the client corresponding to that bit position should benotified.

An interest list table 1700 preferably stores such a bit vector for eachbasket. As the event generator module 1600 receives a triggered basketevent from the PET module 1200, the event generator module 1600 performsa lookup in the interest list table 1700 based on the Basket ID withinthe triggered basket event. As a result of this lookup, the interestlist vector for that Basket ID is retrieved. A formatter 1702 thereafterbuilds an output message event 1704 from the retrieved interest listvector 1706 and the information within the received triggered basketevent, as shown in FIG. 17. Formatter 1702 is preferably configured toadd a header 1708 and trailer 1710 to the output event 1704 such thatthe output event 1704 can be properly interpreted by a client recipient(or recipients) of the output event 1704. The format for such outputevents can comply with any of a number of messaging protocols, bothwell-known (e.g., FAST and FIX) and proprietary. As indicated, the valueof the bit positions in the interest list vector 1706 of the outputevent 1704 will define which clients are to be recipients of the event1704.

The interest list table 1700 is preferably located within availableon-chip memory for the reconfigurable logic device 102. However, itshould be noted that table 1700 could alternatively be stored on anymemory resource within or accessible to coprocessor 140. Also, it shouldbe noted that formatter 1702 may optionally be configured to access oneor more data tables (not shown; either on-chip or off-chip) to definethe appropriate fields for the output event 1704.

It should also be noted that the event generator module 1600 may beconfigured to store statistics such as the number of triggered basketevents it receives, the number of output events it produces, and theaverage inter-event time for each triggered basket event.

FIG. 18 depicts an embodiment wherein pipeline 500 includes a messagequalifier filter module 1800 in communication with the input to the BALmodule 502. The message qualifier filter module 1800 is configured toprocess incoming message events and filter out any message events thatare not of interest to the BCE. Messages of interest will generallyinclude messages which may affect NAV prices. Examples of messages thata practitioner of this embodiment of the invention may want to filterare messages that do not contain a bid, ask, or last price for afinancial instrument. For example, a practitioner may desire to filterout messages which merely correct the previous day's closing prices andmessages relating to trades that do not contribute to the daily volume.

FIG. 19 depicts an exemplary message qualifier filter module 1800 thatcan be employed in the pipeline 500 of FIG. 18. A table 1900 stores aplurality of qualifiers. Module 1800 is preferably configured to compareone or more fields of each incoming message (such as the quote and tradequalifier fields of the incoming messages) against the differentqualifiers stored by table 1900. Each message qualifier is typically asmall binary code which signals a condition. Each qualifier in table1900 preferably maps to an entry in table 1902 which contains apass/drop flag in each address. Thus, if a match is found any of themessage fields with respect to qualifier [1] from table 1900, then thismatch would map to the entry in position n−2 of table 1902, and the bitvalue in position n−2 of table 1902 would be retrieved and provided asan input to AND logic 1904. Message qualifiers from table 1900 can bedirectly addressed to entries in table 1902. However, if the size oftable 1900 were to greatly increase, it should be noted that hashingcould be used to map qualifiers to pass/drop flags.

If any of the inputs to AND logic 1904 are high, then this will causethe pass/drop signal 1906 to follow as high. Logic 1908 is configured tobe controlled by signal 1906 when deciding whether to pass incomingmessages to the output. Thus, if any of the matching qualifiers fromtable 1900 maps to a high bit in table 1902, this will result in thesubject message being passed on to the output of the message qualifierfilter module 1800.

In this manner, each qualifier in the qualifier table may define amessage criteria which, if found in an incoming message, will result inthat incoming message being passed along or blocked from propagatingdownstream in pipeline 500. Examples of message qualifiers which can bestored by table 1900 to indicate that a message should be blocked wouldbe indicators in messages that identify the message as any of a non-firmquote, penalty bid, withdrawn quote, held trade, and out of sequencetrade.

Tables 1900 and 1902 are preferably located within available on-chipmemory for the reconfigurable logic device 102. However, it should benoted that these tables could alternatively be stored on any memoryresource within or accessible to coprocessor 140.

It should also be noted that the incoming messages to pipeline 500 arepreferably normalized messages in that the message fields are formattedin a manner expected by the pipeline 500. Such normalization mayoptionally have been already been performed prior to the time that themessages reach pipeline 500. However, optionally, pipeline 500 mayinclude a message normalization module 2000 as shown in FIG. 20 toprovide the normalization functions. Such normalization preferablyincludes functions such as symbol ID mapping (wherein each message isassigned with a symbol ID (preferably a fixed-size binary tag) thatuniquely identifies the financial instrument which is the subject of themessage) and GEID mapping (wherein each message is assigned with a GEID(preferably also a fixed-size binary tag) that uniquely identifies theexchange corresponding to the message). FAM modules to perform suchnormalization functions are described in the above-referenced andincorporated U.S. Patent Application Publication 2008/0243675.

With respect to administering the BCE 400, it should be noted thatbaskets and trigger conditions can be created, modified, and/or deletedin any of a number of ways. For example, a system administrator and/orclient application can be given the ability to define baskets and/ortrigger conditions

To create and/or modify a basket, appropriate entries and allocationswill be needed for the new/modified basket in the various tablesdescribed herein in connection with pipeline 500. A system administratormay use a control interface to add/modify basket configurations. Clientapplications may submit basket definitions via an applicationprogramming interface (API) that runs on their local machine(s).

The BCE pipeline may be deployed on coprocessor 140 of a ticker plantplatform 2100 as shown in FIG. 21. Additional details regarding such aticker plant platform 2100 can be found in the above-referenced andincorporated U.S. Patent Application Publication 2008/0243675. Insummary, the financial market data from the exchanges is received at theO/S supplied protocol stack 2102 executing in the kernel space onprocessor 112 (see FIGS. 1(a)-(b)). An upject driver 2104 delivers thisexchange data to multi-threaded feed pre-processing software 2112executing in the user-space on processor 112. These threads may thencommunicate data destined for the coprocessor 140 to the hardwareinterface driver software 2106 running in the kernel space.

Instructions from client applications may also be communicated to thehardware interface driver 2106 for ultimate delivery to coprocessor 140to appropriately configure a BCE pipeline that is instantiated oncoprocessor 140. Such instructions arrive at an O/S supplied protocolstack 2110 from which they are delivered to a request processingsoftware module 2116. A background and maintenance processing softwaremodule 2114 thereafter determines whether the client application has theappropriate entitlement to add/change/modify a basket. If so entitled,the background and maintenance processing block 2114 communicates acommand instruction to the hardware interface driver 2106 for deliveryto the coprocessor to appropriately update the table entries in the BCEpipeline to reflect the added/changed/modified basket, as previouslyexplained above. Deletions of baskets as well asadditions/modifications/deletions of trigger conditions from the BCEpipeline can be performed via a like process, as indicated above.

The hardware interface driver 2106 then can deliver an interleavedstream of financial market data and commands to the coprocessor 140 forconsumption thereby. Outgoing data from the coprocessor 140 returns tothe hardware interface driver 2106, from which it can be supplied to MDCdriver 2108 for delivery to the client connections (via protocol stack2110) and/or delivery to the background and maintenance processing block2114.

As discussed above, some financial instrument baskets such as ETFs mayhave some form of cash component to them. These cash components can betaken into account by the system in any of a number of ways.

For example, to insert a cash component into a basket's NAV, a controlprocess can be configured to generate a synthetic event for delivery tothe pipeline. A symbol ID would be assigned to a given cash account, andthe price for that synthetic symbol ID would be $1 and the weight valuew for that cash account would be set equal to the cash value of the cashaccount. The tables used by the BAL module would be updated to reflectthis synthetic symbol ID and cause the cash account to impact the NAVfor the basket. In a more complex scenario, events could be delivered tothe pipeline so that the value of the cash account can fluctuate withthe value of the pertinent currency on the foreign exchange market.

Another way of injecting a cash component of a basket into the system isto define the trigger threshold values in tables 1314, 1316, 1318, 1320,and 1322 such that the cash component amounts are built into thosetrigger threshold values.

Also, it should be noted that the BAL module (or the BVU module) can beconfigured to drop events where all of the delta prices for that eventare zero.

Similarly, it should be noted that in the embodiment of FIG. 9, the NAVupdate engine 950 is configured to compute updated NAVs even for NAVtypes whose corresponding delta prices are zero. That is, a currentdelta event being processed by the BVU module may contain a $0.03 valuefor the ΔBid price but zero values for the ΔAsk and ΔLast prices. Withthe embodiment of FIG. 9, some of the different NAV compute logicengines 916 of the parallel paths would thus be used to compute a newNAV where there will not be a change relative to the old NAV (e.g., forthe example given, the Ask NAV^(new), Last NAV^(new), and Ask+TickNAV^(new) values would be unchanged relative to their old values). It isbelieved that this configuration will still yield effective efficiencygiven that most of the events processed by the pipeline are expected tocomprise quote activity. However, a practitioner is free to choose toincrease the efficiency of the BVU module by not including any zerodelta prices in the delta events and including a dynamic scheduler andmemory controller which directs delta events to the appropriatecomputing path(s) within the NAV update engine 950 to more fully utilizeall available computing paths within the NAV update engine 950.

It should also be noted that logic could be added to the BVU modulessuch that the NAV computations are also based on derived statistics suchas the Volume Weighted Average Price (VWAP) and use other fields whichmay be available in a message, such as the difference in traded volume.

It should also be noted that the coprocessor 140 can be configured toperform computations in addition to those of the BCE if desired by apractitioner of an embodiment of the invention. For example, thecoprocessor 140 can also employ a last value caching (LVC) module, asdescribed in the above-referenced and incorporated U.S. PatentApplication Publication 2008/0243675.

It should be noted that a practitioner of an embodiment of the inventionmay choose to compute the value for Δj but not the value for NAV^(new),in which case the BVU module need not produce any NAV^(new) values. Insuch instances, the trigger conditions used by the PET module 1200 canbe built around variation in Δj rather than NAV^(new). In such asituation, the client could optionally determine the NAV values itselffrom the Δj value, although a practitioner may find value in the Δjvalues themselves apart from their actual combination with an old NAVvalue to find a new NAV value.

Also, while the preferred embodiment for the BAL module 502 isconfigured to compute the delta values for the last/bid/ask prices inthe incoming messages, it should be noted that the computation of suchdelta values can optionally be performed by one or more modules upstreamfrom the BAL module 502 or the BCE pipeline 500 altogether. As such, theinput messages to the BCE pipeline 500 may already contain the ΔBid,ΔAsk, and/or ΔLast prices for the subject financial instruments. In suchinstances, table 608 would not need to store the previous bid/ask/lastprices for each financial instrument, and BAL module 502 need not employthe logic necessary to compute those delta prices.

Furthermore, while in the preferred embodiment disclosed herein thecoprocessor 140 comprises a reconfigurable logic device 102 such as anFPGA, it should be noted that the coprocessor 140 can be realized usingother processing devices. For example, the coprocessor 140 may comprisegraphics processor units (GPUs), general purpose graphics processors,chip multi-processors (CMPs), dedicated memory devices, complexprogrammable logic devices, application specific integrated circuits(ASICs), and other I/O processing components. Moreover, it should benoted that system 100 may employ a plurality of coprocessors 140 ineither or both of a sequential and a parallel multi-coprocessorarchitecture.

Further still, while the inventors believe that special advantages existin connection with using the pipeline disclosed herein to processfinancial information and compute updated values for financialinstrument baskets, the inventors further note that the disclosedpipeline also provides latency advantages when processing non-financialdata which pertains to baskets. For example, the basket calculationengine may be configured to compute the net asset value of the inventoryheld by a multi-national retail organization. Incoming events to thepipeline that correspond to product sales and/or product deliveriescould thus be used to update stored values for each product, whereinthese stored values correspond to inventory count values. A weightassigned to each product would correspond to a price value assigned tothe product. Thus, a pipeline such as the one disclosed herein could beused to closely track the net inventory values of the organization'sproducts. Triggers may then be used to notify management when inventoryvalues swell or shrink by a given threshold.

Another example would be for a basket which is a collection of datapoints from some type of scientific experiment. Each data point may haveassociated values such as size, mass, etc., and these data points mayarrive over time as streaming data from one or more monitoring stations.The pipeline can readily be configured to derive a size NV by computinga weighted sum of the sizes from the incoming data stream and/or a massNV by computing a weighted sum of the masses from the incoming datastream.

While the present invention has been described above in relation to itspreferred embodiments, various modifications may be made thereto thatstill fall within the invention's scope. Such modifications to theinvention will be recognizable upon review of the teachings herein.Accordingly, the full scope of the present invention is to be definedsolely by the appended claims and their legal equivalents.

What is claimed is:
 1. A system for low latency basket calculation, the system comprising: a member of the group consisting of (1) a reconfigurable logic device, (2) a graphics processor unit (GPU), and (3) a chip multi-processor (CMP), wherein the member comprises a processing pipeline configured to receive streaming messages that comprise a plurality of data values for a plurality of elements, the processing pipeline comprising a basket association lookup module and a downstream basket value updating module; wherein the basket association lookup module is configured to retrieve stored data based on the messages, the retrieved data corresponding to a plurality of baskets, the baskets having at least one of the elements as a basket member; wherein the basket value updating module is configured to compute a plurality of net values for the baskets based on the retrieved data and the messages; and wherein the basket association lookup module and the basket value updating module are configured to operate with respect to different messages and/or baskets at the same time in parallel with each other.
 2. The system of claim 1 wherein each net value of a plurality of the net values comprises an aggregation of a plurality of the data values for the basket member elements of the basket corresponding to that net value.
 3. The system of claim 2 wherein the basket value updating module comprises: a plurality of parallel computing paths, each computing path comprising compute logic configured to compute a plurality of the net values such that the computing paths are configured to simultaneously compute a plurality of different net values for the baskets.
 4. The system of claim 2 wherein each of a plurality of the streaming messages further comprises identifying data for the element to which that streaming message pertains; and wherein the basket association lookup table is further configured to determine the baskets to which the messages pertain based on the identifying data.
 5. The system of claim 4 further comprising: a memory configured to store a basket set pointer table and a basket set table, wherein the basket set pointer table comprises a plurality of basket set pointer table entries, and wherein the basket set table comprises a plurality of basket set table entries; wherein the basket set pointer table entries are configured to associate the identifying data for the elements with pointers to the basket set table entries; wherein the basket set table entries are configured to associate the elements with the baskets of which the elements are basket members, each of a plurality of the basket set table entries corresponding to an element and comprising (1) at least one basket identifier and weight pair, wherein the basket identifier identifies a basket of which that element is a basket member, and wherein the weight identifies a weight of that element in the identified basket, and (2) a previous data value for that element; and wherein the basket set association lookup module is further configured to, for a streaming message, (1) retrieve the pointer from the basket set pointer table that is associated with the identifying data of that streaming message, (2) retrieve the basket set table entry corresponding to the retrieved pointer, and (3) provide data from the retrieved basket set table entry to the basket value updating module.
 6. The system of claim 5 wherein the basket association lookup module is further configured to compute a delta value indicative of a difference between (1) the previous data value from the retrieved basket set table entry and (2) the data value in the streaming message corresponding the retrieved basket set table entry, and wherein the delta value is included in the provided data.
 7. The system of claim 5 wherein the memory is external to the member.
 8. The system of claim 5 wherein the memory is internal to the member.
 9. The system of claim 5 wherein the basket value updating module is further configured to compute the net values according to a delta calculation approach.
 10. The system of claim 9 wherein the provided data comprises a plurality of delta events, and wherein the system further comprises a buffer between the basket association lookup module and the basket value updating module, wherein the basket association lookup module is configured to write the provided data to the buffer as the delta events, and wherein the basket value updating module is configured to (1) read the delta events from the buffer, and (2) compute the net values for the baskets based on the read delta events.
 11. The system of claim 1 wherein the processing pipeline further comprises a price event trigger module downstream from the basket value updating module, the price event trigger module being configured to process the computed net basket values against a triggering condition to determine whether any of the computed net basket values are to be reported to a client; and wherein the basket association lookup module, the basket value updating module, and the price event trigger module are arranged in a pipelined manner.
 12. The system as defined in claim 11 wherein the processing pipeline further comprises an event generator module downstream from the price event trigger module, wherein the event generator module is configured to generate a message for delivery to the client in response to a determination by the price event trigger module that a computed net basket value is to be reported to the client, the generated message comprising the computed net basket value; and wherein the basket association lookup module, the basket value updating module, the price event trigger module, and the event generator module are arranged in a pipelined manner.
 13. The system of claim 1 wherein the member comprises the reconfigurable logic device.
 14. The system of claim 13 wherein the reconfigurable logic device comprises a field programmable gate array (FPGA).
 15. The system of claim 1 wherein the member comprises the GPU.
 16. The system of claim 1 wherein the member comprises the CMP.
 17. The system of claim 1 wherein the streaming messages comprises a plurality of financial market data messages for a plurality of financial instruments, and wherein the baskets comprise financial instrument baskets.
 18. The system of claim 1 the streaming messages comprise messages about product sales and/or product deliveries, and wherein the baskets comprise inventory baskets.
 19. The system of claim 1 wherein the streaming messages comprise data about monitored events from one or more monitoring stations, and wherein the baskets comprise data point baskets for the monitored events.
 20. The system of claim 1 wherein the member serves as a coprocessor for the system, the system further comprising: a processor for communication with the member, wherein the processor is configured to manage a delivery of the streaming messages to the member.
 21. The system of claim 20 wherein the processor comprises a general purpose processor (GPP) configured to execute software to manage the delivery of the streaming messages to the member.
 22. A system for low latency basket calculation, the system comprising: a field programmable gate array (FPGA), the FPGA comprising a firmware pipeline, the firmware pipeline configured to receive a message, the message comprising (1) identifying data for an element, and (2) a data value for the element; wherein the firmware pipeline comprises: first hardware logic configured to determine at least one basket of which the element is a basket member based on the identifying data; and second hardware logic configured to compute a net value for each determined basket based on the data value; wherein the FPGA is further configured to stream a plurality of messages through the firmware pipeline such that the first and second hardware logic are configured to operate in a pipelined fashion wherein the first hardware logic is configured to repeatedly operate with respect to a new message while the second hardware logic is configured to repeatedly operate with respect to a message previously operated on by the first hardware logic.
 23. The system of claim 22 wherein the second hardware logic comprises a plurality of parallel computing paths configured to compute a plurality of net values for the baskets in parallel.
 24. The system of claim 22 further comprising: a basket set table configured to store a plurality of basket set table entries that associate elements with (1) baskets of which the elements are member, and (2) data for supporting the net value computation; and a basket set pointer table configured to store a plurality of pointers to the basket set table entries, wherein the pointers are associated with the identifying data for the elements; and wherein the first hardware logic, for each of a plurality of the streaming messages, is further configured to: retrieve a pointer from the basket set pointer table entry that is associated by the basket set pointer table with the identifying data of that streaming message; retrieve data from the basket set table entry corresponding to the retrieved pointer, wherein the retrieved data comprises, for each basket of which the element is a basket member, (1) a basket identifier and weight pair, wherein the basket identifier serves to identify a basket of which the element is a member, and wherein the weight serves to identify a weight of the element in that basket, and (2) a previous data value for the element.
 25. The system of claim 24 wherein the basket set table and the basket set pointer table are resident in the FPGA.
 26. The system of claim 24 wherein the second hardware logic is further configured to compute, for each of a plurality of the baskets corresponding to the basket identifiers in the retrieved data, the net value for that basket, wherein the computed net value is based on the weight, the previous data value within the retrieved data, and the data value in the streaming message.
 27. The system of claim 24 wherein the second hardware logic is configured to compute the net values according to a delta calculation approach using data within the retrieved data.
 28. A method for low latency basket calculation, the method comprising: receiving a message, the message comprising (1) identifying data for an element, and (2) a data value for the element; determining at least one basket of which the element is a basket member based on the identifying data; computing a net value for each determined basket based on the data value; repeating the method steps for a plurality of streaming messages; and wherein the determining and computing steps are performed by a member of the group consisting of (1) a reconfigurable logic device, (2) a graphics processor unit (GPU), and (3) a chip multi-processor (CMP), and wherein the member performs the determining and computing steps in a pipelined fashion such that the determining step is performed with respect to a new streaming message while the computing step is performed with respect to a previous streaming message.
 29. The method of claim 28 wherein the determining step comprises: accessing a basket set pointer table; retrieving a pointer from the accessed basket set pointer table that is associated by the basket set pointer table with the identifying data; accessing a basket set table based on the retrieved pointer; and retrieving data from the accessed basket set table that is associated by the basket set table with the retrieved pointer, wherein the retrieved data comprises, for each basket of which the element is a basket member, (1) a basket identifier and weight pair, wherein the basket identifier serves to identify a basket of which the element is a member, and wherein the weight serves to identify a weight of the element in that basket, and (2) a previous data value for the element.
 30. The method of claim 29 wherein the computing step comprises: computing the net value for a basket identified by a basket identifier within the retrieved data, wherein the computed net value is based on the weight, the previous data value within the retrieved data, and the data value in the streaming message.
 31. The method of claim 28 wherein the computing step comprises computing in parallel a plurality of net values for the determined basket via a plurality of parallel computing paths.
 32. The method of claim 28 wherein the computing step comprises computing the net value according to a delta calculation approach. 